Real time monitoring of driver’s attention and fatigue typically is approached in controlled conditions, which cannot be valid for real-world applications. This work focuses on the use of deep learning to classify the inattentive states providing a classification model independent from a specific training for each single user. The database for the experiment is collected through a camera mounted on the car dash, recording live video with adverse light conditions. The proposed system proved to have high accuracy at a low computational cost with a fast processing time, viable for real time car applications, and tested in a ground-truth environment, far from the favourable controlled conditions of a simulator. Moreover, this approach offers an improvement in the classification stage, being error-free when pre-training the system in the detection of fatigue and inattention of a specific user and, with minimal error on a random user dataset.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Detection of driver’s inattention: a real-time deep learning approach


    Beteiligte:
    Tryhub, S. (Autor:in) / Masala, G. L. (Autor:in)


    Erscheinungsdatum :

    2019-10-01


    Format / Umfang :

    449594 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    METHOD AND APPARATUS FOR JUDGING DRIVER'S INATTENTION

    SONG HYUN SEOK / LEE IN HO / YANG TAE HYOUNG | Europäisches Patentamt | 2015

    Freier Zugriff

    Driver Inattention Detection

    Rezaei, Mahdi / Klette, Reinhard | Springer Verlag | 2017


    Real-Time Driver's Stress Event Detection

    Rigas, G | Online Contents | 2012


    Real-time driver's eye state detection

    Zhichao Tian, / Huabiao Qin, | IEEE | 2005


    Real-Time Driver's Stress Event Detection

    Rigas, G. / Goletsis, Y. / Fotiadis, D. I. | IEEE | 2012